Record 1 of 6
|
|
Author(s):
| Chen SJ; Carroll JD
|
Title:
| 3-D reconstruction of coronary arterial
tree to optimize angiographic visualization
|
Source:
| IEEE TRANSACTIONS ON MEDICAL IMAGING
2000, Vol 19, Iss 4, pp 318-336
|
No. cited references:
| 56
|
ISSN/ISBN:
| 0278-0062
|
Publisher:
| IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS
INC
|
Addresses:
| Chen SJ, Univ Colorado, Hlth Sci Ctr,
Dept Med, Div Cardiol, Denver, CO 80262 USA
Univ Colorado, Hlth Sci Ctr, Dept Med, Div Cardiol, Denver, CO 80262 USA
|
Author Keywords:
| angiography; catheterization; three-dimensional
reconstruction; vasculature
|
KeywordsPlus:
| 3-DIMENSIONAL RECONSTRUCTION; BIPLANE
ANGIOGRAMS; VASCULAR TREE; RIGID OBJECTS; MOTION; VIEWS; SYSTEM; SEGMENTATION;
PROJECTIONS; ALGORITHMS
|
Abstract:
| Due to vessel overlap and foreshortening,
multiple projections are necessary to adequately evaluate the coronary
tree with arteriography, Catheter-based interventions can only be optimally
performed when these visualization problems are successfully solved. The
traditional method provides multiple selected views in which overlap and
foreshortening are subjectively minimized based on two dimensional (2-D)
projections. A pair of images acquired from routine angiographic study
at arbitrary orientation using a single-plane imaging system were chosen
far three-dimensional (3-D) reconstruction. After the arterial segment
of interest (e.g., a single coronary stenosis or bifurcation lesion) was
selected, a set of gantry angulations minimizing segment foreshortening
was calculated. Multiple computer-generated projection images with minimized
segment foreshortening were then used to choose views with minimal overlapped
vessels relative to the segment of interest. The optimized views could
then be utilized to guide subsequent angiographic acquisition and interpretation.
Over 800 cases of coronary arterial trees have been reconstructed, in
which more than 40 cases were performed in room during cardiac catheterization.
The accuracy of 3-D length measurement was confirmed to be within an average
root-mean-square (rms) 3.5% error using eight different pairs of angiograms
of an intracoronary guidewire of 105-mm length with eight radiopaque markers
of 15-mm interdistance. The accuracy of similarity between the additional
computer-generated projections versus the actual acquired views was demonstrated
with the average rms errors of 3.09 mm and 3.13 mm in 20 LCA and 20 RCA
cases, respectively. The projections of the reconstructed patient-specific
3-D coronary tree model can be utilized for planning optimal clinical
views: minimal overlap and foreshortening, The assessment of lesion length
and diameter narrowing can be optimized in both interventional cases and
studies of disease progression and regression.
|
Cited references:
| CARROLL JD-1996-CIRCULATION-V94-P1376
CARROLL JD-1998-J-AM-COLL-CARDIOL-A-V31-PA139
CHEN SY-1992-SPIE-P-OPTICAL-ENG-M-V1778-P14
CHEN SYJ-1997-CIRCULATION-V96-P1290
CHEN SYJ-1997-MED-PHYS-V24-P633
CHEN SYJ-1996-P-SOC-PHOTO-OPT-INS-V2710-P103
CHEN SYJ-1997-P-SPIE-MED-IM-1997-I-V3034-P25
CHEN SYJ-1998-WNATS-NEW-CARDIOVASC-P61
CHERIET F-1994-P-SOC-PHOTO-OPT-INS-V2354-P279
COATRIEUX JL-1992-INT-J-CARDIAC-IMAG-V8-P1
COPPINI G-1991-MED-BIOL-ENG-COMPUT-P535
DELAERE D-1991-MED-BIOL-ENG-COMPUT-V29-P27
DUMAY ACM-1994-IEEE-T-MED-IMAGING-V13-P13
FANG JQ-1984-IEEE-T-PATTERN-ANAL-V6-P547
FENCIL LE-1990-MED-PHYS-V17-P951
FESSLER JA-1991-IEEE-T-MED-IMAGING-V10-P25
FINET G-1995-INT-J-CARDIAC-IMA-S1-V1-P53
GARREAU M-1991-IEEE-T-MED-IMAGING-V10-P122
GUGGENHEIM N-1991-PHYS-MED-BIOL-V36-P99
HAMILTON WR-1969-ELEMENTS-QUATERNIONS
HORN B-1996-ROBOT-VISION
KASS M-1988-INT-J-COMPUT-VISION-V2-P321
KEATING TJ-1975-PHOTOGRAMMETRIC-ENG-V41-P993
KIM HC-1982-IEEE-T-MED-IMAGING-V1-P152
KITAMURA K-1988-IEEE-T-MED-IMAGING-V7-P173
LIU IH-1992-OPT-ENG-V31-P2197
LONGUETHIGGINS HC-1981-NATURE-V293-P133
MARCUS ML-1991-CARDIAC-IMAGING-COMP-P24
METZ CE-1989-MED-PHYS-V16-P45
MUIJTJENS AMM-1995-IEEE-COMPUT-CARDIOL-P577
NGUYEN TV-1994-IEEE-T-MED-IMAGING-V13-P61
PARKER DL-1987-COMPUT-BIOMED-RES-V20-P166
PELLOT C-1994-IEEE-T-MED-IMAGING-V13-P48
PERVIN E-1983-P-IEEE-C-COMP-VIS-PA
PHILIP J-1991-IEEE-T-PATTERN-ANAL-V13-P61
PRAUSE GPM-1996-P-SOC-PHOTO-OPT-INS-V2709-P82
ROUGEE A-1994-INT-J-CARDIAC-IMAG-V10-P67
SAITO T-1990-IEEE-T-BIO-MED-ENG-V37-P768
SATO Y-1998-IEEE-T-MED-IMAGING-V17-P121
SEILER C-1992-CIRCULATION-V85-P1987
SITOMER J-1988-P-COMPUTERS-CARDIOLO-V87-P192
SMETS C-1990-INT-J-CARDIAC-IMAG-V5-P145
SOLZBACH U-1994-COMPUT-BIOMED-RES-V27-P178
SONKA M-1993-IEEE-T-MED-IMAGING-V12-P588
STANSFIELD SA-1986-IEEE-T-PATTERN-ANAL-V8-P188
TSAI RY-1984-IEEE-T-PATTERN-ANAL-V6-P13
WAHLE A-1995-IEEE-T-MED-IMAGING-V14-P230
WATKINS DS-1991-FUNDAMENTALS-MATRIX
WENG J-1988-P-IEEE-C-COMP-VIS-PA-P381
WENG J-1987-P-IEEE-WORKSH-COMP-V-P355
WENG JY-1993-IEEE-T-PATTERN-ANAL-V15-P864
WENG JY-1989-IEEE-T-PATTERN-ANAL-V11-P451
WEYMAN AE-1982-CROSS-SECTIONAL-ECHO
WOLLSCHLAGER H-1986-IEEE-COMPUT-CARDIOL-P185
YANAGIHARA Y-1994-INT-J-CARDIAC-IMAG-V10-P253
YEN BL-1983-COMPUT-VISION-GRAPH-V21-P21
|
Times Cited:
| 0
|
Source item page count:
| 19
|
Publication Date:
| APR
|
IDS No.:
| 333AP
|
29-char source abbrev:
| IEEE TRANS MED IMAGING
|
Publisher address:
| 345 E 47TH ST, NEW YORK, NY 10017-2394
USA
|
Record 2 of 6
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|
Author(s):
| Haris K; Efstratiadis SN; Maglaveras
N; Pappas C; Gourassas J; Louridas G
|
Title:
| Model-based morphological segmentation
and labeling of coronary angiograms
|
Source:
| IEEE TRANSACTIONS ON MEDICAL IMAGING
1999, Vol 18, Iss 10, pp 1003-1015
|
No. cited references:
| 45
|
ISSN/ISBN:
| 0278-0062
|
Publisher:
| IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS
INC
|
Addresses:
| Maglaveras N, Aristotelian Univ Salonika,
Fac Med, Lab Med Informat, Sch Med, GR-54006 Salonika, Greece
Aristotelian Univ Salonika, Fac Med, Lab Med Informat, Sch Med, GR-54006 Salonika, Greece Technol Educ Inst Thessaloniki, Sch Technol Applicat, Dept Informat, Sindos 54101, Greece Aristotelian Univ Salonika, Cardiol Clin, AHEPA Gen Hosp, Sch Med, GR-54006 Salonika, Greece
|
Author Keywords:
| angiography; artery tracking; artery
tree labeling; coronary quantitative graph matching; mathematical morphology;
segmentation
|
KeywordsPlus:
| MAXIMUM CLIQUE PROBLEM; VASCULAR NETWORKS;
IMAGES; RECONSTRUCTION; WATERSHEDS; ALGORITHM; ARTERIES; TRACKING; BORDERS;
TREES
|
Abstract:
| A method for extraction and labeling
of the coronary arterial tree (CAT) using minimal user supervision in
single-view angiograms is proposed. The CAT structural description (skeleton
and borders) is produced, along with quantitative information for the
artery dimensions and assignment of coded labels, based on a given coronary
artery model represented by a graph. The stages of the method are: 1)
CAT tracking and detection; 2) artery skeleton and border estimation;
3) feature graph creation; and iv) artery labeling by graph matching.
The approximate CAT centerline and borders are extracted by recursive
tracking based on circular template analysis. The accurate skeleton and
borders of each CAT segment are computed, based on morphological homotopy
modification and watershed transform. The approximate centerline and borders
are used for constructing the artery segment enclosing area (ASEA), where
the defined skeleton and border curves are considered as markers. Using
the marked ASEA, an artery gradient image is constructed where all the
ASEA pixels (except the skeleton ones) are assigned the gradient magnitude
of the original image. The artery gradient image markers are imposed as
its unique regional minima by the homotopy modification method, the watershed
transform is used for extracting the artery segment borders, and the feature
graph is updated. Finally, given the created feature graph and the known
model graph, a graph matching algorithm assigns the appropriate labels
to the extracted CAT using weighted maximal cliques on the association
graph corresponding to the two given graphs. Experimental results using
clinical digitized coronary angiograms are presented.
|
Cited references:
| BALLARD D-1982-COMPUTER-VISION
BESL PJ-1988-IEEE-T-PATTERN-ANAL-V10-P167
CARRAGHAN R-1990-OPER-RES-LETT-V9-P375
CHALOPIN C-1998-P-COMP-CARD-98-P761
CHEN SYJ-1997-MED-PHYS-V24-P633
COPPINI G-1993-IEEE-T-PATTERN-ANAL-V15-P156
DEFEYTER PJ-1995-QUANTITATIVE-CORONAR
DERZWET PMJ-1998-IEEE-T-MED-IMAGING-V17-P108
DETRE KM-1975-CIRCULATION-V52-P979
DODGE JT-1992-CIRCULATION-V86-P232
DODGE JT-1988-CIRCULATION-V78-P1167
DUMARY AC-1996-YB-MED-INFORMATICS-P353
EZQUERRA N-1998-IEEE-T-MED-IMAGING-V17-P429
FABER TL-1996-P-COMP-CARD-96-P333
FIGUEIREDO MAT-1995-IEEE-T-MED-IMAGING-V14-P162
FOZZARD HA-1992-HEART-CARDIOVASCULAR-V1
GARREAU M-1991-IEEE-T-MED-IMAGING-V10-P122
HALL P-1997-IEEE-T-MED-IMAGING-V16-P919
HARIS K-1998-IEEE-T-IMAGE-PROCESS-V7-P1684
HARIS K-1997-P-COMP-CARD-97-P741
HARIS K-1998-P-COMP-CARD-98-P769
HART M-1993-P-COMP-CARD-93-P93
HORAUD R-1989-IEEE-T-PATTERN-ANAL-V11-P1168
KLEIN AK-1997-IEEE-T-MED-IMAGING-V16-P468
LIU IC-1993-IEEE-T-MED-IMAGING-V12-P334
LU S-1993-P-COMPUTERS-CARDIOLO-P575
MEYER F-1990-J-VIS-COMMUN-IMAGE-R-V1-P21
NAJMAN L-1998-IEEE-T-PATTERN-ANAL-V18-P1163
NGUYEN TV-1994-IEEE-T-MED-IMAGING-V13-P61
PAPPAS TN-1988-IEEE-T-ACOUST-SPEECH-V36-P1501
PARDALOS PM-1994-J-GLOBAL-OPTIM-V4-P301
PISUPATI C-1996-P-ACM-S-COMP-GEOM-PH
ROUEN TAD-1977-CIRCULATION-V55-P324
SAITO T-1990-IEEE-T-BIO-MED-ENG-V37-P768
SERRA J-1982-IMAGE-ANAL-MATH-MORP
SMETS C-1990-INT-J-CARDIAC-IMAG-V5-P145
SONKA M-1995-IEEE-T-MED-IMAGING-V14-P151
SONKA M-1993-IEEE-T-MED-IMAGING-V12-P588
SUETENS P-1992-ACM-COMPUT-SURVEYS-V24
SUN Y-1989-IEEE-T-MED-IMAGING-V8-P78
TOM BCS-1994-IEEE-T-MED-IMAGING-V13-P450
TRAN LV-1992-IEEE-T-MED-IMAGING-V11-P517
VINCENT L-1993-IEEE-T-IMAGE-PROCESS-V2-P176
VINCENT L-1991-IEEE-T-PATTERN-ANAL-V13-P583
XIA WX-1992-IEEE-T-MED-IMAGING-V11-P153
|
Times Cited:
| 0
|
Source item page count:
| 13
|
Publication Date:
| OCT
|
IDS No.:
| 269NQ
|
29-char source abbrev:
| IEEE TRANS MED IMAGING
|
Publisher address:
| 345 E 47TH ST, NEW YORK, NY 10017-2394
USA
|
Record 3 of 6
|
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Author(s):
| Grigorishin T; Abdel-Hamid G; Yang
YH
|
Title:
| Skeletonisation: An electrostatic
field-based approach
|
Source:
| PATTERN ANALYSIS AND APPLICATIONS
1998, Vol 1, Iss 3, pp 163-177
|
No. cited references:
| 44
|
ISSN/ISBN:
| 1433-7541
|
Publisher:
| SPRINGER VERLAG
|
Addresses:
| Yang YH, Univ Saskatchewan, Dept Comp
Sci, Comp Vis & Graph Lab, Scene Anal & Modelling Grp, Saskatoon,
SK S7N 5A9, Canada
Univ Saskatchewan, Dept Comp Sci, Comp Vis & Graph Lab, Scene Anal & Modelling Grp, Saskatoon, SK S7N 5A9, Canada
|
Author Keywords:
| corner detection; electrostatic-field;
shape analysis; skeletonisation
|
KeywordsPlus:
| SHAPE REPRESENTATION; THINNING ALGORITHM;
PLANAR CURVES; COMPUTATION; MODEL
|
Abstract:
| Skeleton representation of an object
is a powerful shape descriptor that captures both boundary and region
information of the object. The skeleton of a shape is a representation
composed of idealized thin lines that preserve the connectivity or topology
of the original shape. Although the literature contains a large number
of skeletonisation algorithms, many open problems remain. In this paper,
we present a new skeletonisation approach that relies on the Electrostatic
Field Theory (EFT). Many problems associated with existing skeletonisation
algorithms are solved using the proposed approach. In particular, connectivity,
thinness and other desirable features of a skeleton are guaranteed. It
also captures notions of corner detection, multiple scale, thinning, and
skeletonisation all within one unified framework. The performance of the
proposed EFT-based algorithm is studied extensively. Using the Hausdorf
distance measure, the noise sensitivity of the algorithm is compared to
two existing skeletonisation techniques. In addition, the experimental
results also demonstrate the multiscale property of the proposed approach.
|
Cited references:
| *KROH RES INC-1994-KHOR
ABDELHAMID GH-1993-P-1993-CAN-C-EL-COMP-P767
AHUJA N-1997-IEEE-T-PATTERN-ANAL-V19-P169
ARCELLI C-1981-COMPUTER-GRAPHICS-IM-V17-P130
ARCELLI C-1989-IEEE-T-PATTERN-ANAL-V4-P411
ARCELLI C-1985-IEEE-T-PATTERN-ANAL-V7-P463
ARUMUGAM A-1993-INT-J-PATTERN-RECOGN-V7-P988
AURENHAMMER F-1991-COMPUT-SURV-V23-P345
BLUM H-1967-MODELS-PERCEPTION-SP
BRANDT JW-1992-CVGIP-IMAG-UNDERSTAN-V55-P329
DANIELSSON PE-1980-COMPUT-GRAPHICS-IMAG-V14-P227
DILL AR-1987-IEEE-T-PATTERN-ANAL-V9-P495
FULLER AJB-1973-ENG-FIELD-THEORY
GAUCH J-1992-IEEE-T-PATTERN-ANAL-V15-P753
GIARDINA CR-1988-MORPHOLOGICAL-METHOD
GRIGORISHIN T-1997-FORM-SEGMENTATION-EL
GRIGORISHIN T-1998-VISION-INTERFACE-98
GUO ZC-1992-CVGIP-IMAG-UNDERSTAN-V55-P317
HARALICK RM-1992-COMPUTER-ROBOT-VISIO
HARALICK RM-1992-PATTERN-RECOGN-LETT-V13-P5
JAISIMHA MY-1993-P-2-INT-C-DOC-AN-REC-P282
KLEIN F-1987-PATTERN-RECOGN-V3-P19
LAM L-1992-IEEE-T-PATTERN-ANAL-V14-P869
LAM L-1992-P-11-INT-C-PATT-REC-P342
LEE SW-1991-P-1-INT-C-DOC-AN-REC-P260
LEYMARIE F-1992-IEEE-T-PATTERN-ANAL-V14-P56
LEYMARIE F-1990-THESIS-MCGILL-U-MONT
MARTINEZPEREZ MP-1987-COMPUT-VISION-GRAPH-V39-P186
MAYA N-1995-PATTERN-RECOGN-V16-P147
MITTRA R-1971-ANAL-TECHNIQUES-THEO-P4
MOKHTARIAN F-1992-IEEE-T-PATTERN-ANAL-V14-P789
MOKHTARIAN F-1986-IEEE-T-PATTERN-ANAL-V8-P34
NGUYEN TV-1994-IEEE-T-MED-IMAGING-V13-P61
NUSSBAUM A-1967-FIELD-THEORY
OGNIEWICZ RL-1992-P-IEEE-C-VIS-PATT-RE-P63
PAVLIDIS T-1982-COMPUTER-GRAPHICS-IM-V20-P133
PIECH MA-1988-COMPUT-VISION-GRAPH-V42-P381
PLAMONDON R-1988-P-VIS-INT-1988-EDM-C-P70
SHAPIRO B-1981-COMPUT-GRAPHICS-IMAG-V15-P136
SHIN FY-1995-PATTERN-RECOGN-V28-P331
SHIN FYC-1992-IEEE-T-IMAGE-PROCESS-V1-P197
SILVESTER P-1967-MODERN-ELECTROMAGNET
SMITH RW-1987-PATTERN-RECOGN-V20-P7
VEGA OE-1994-CVGIP-IMAG-UNDERSTAN-V60-P285
|
Times Cited:
| 0
|
Source item page count:
| 15
|
IDS No.:
| 256CA
|
29-char source abbrev:
| PATTERN ANAL APPL
|
Publisher address:
| 175 FIFTH AVE, NEW YORK, NY 10010
USA
|
Record 4 of 6
|
|
Author(s):
| Ezquerra N; Capell S; Klein L; Duijves P
|
Title:
| Model-guided labeling of coronary structure
|
Source:
| IEEE TRANSACTIONS ON MEDICAL IMAGING 1998, Vol 17, Iss 3, pp 429-441
|
No. cited references:
| 42
|
ISSN/ISBN:
| 0278-0062
|
Publisher:
| IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
|
Addresses:
| Ezquerra N, Georgia Inst Technol, Coll Comp, Mail Code 0280, Atlanta, GA 30332 USA Georgia Inst Technol, Coll Comp, Atlanta, GA 30332 USA Emory Univ, Atlanta, GA 30322 USA Delft Univ Technol, Delft, Netherlands
|
Author Keywords:
| angiographic imaging; dynamic programming; image labeling; image understanding; medical imaging
|
KeywordsPlus:
| ORTHOGONAL PROJECTIONS; VASCULAR NETWORKS; RECONSTRUCTION; ANGIOGRAMS; SEGMENTATION; ARTERIES; IMAGES; SYSTEM
|
Abstract:
| Assigning anatomic labels to coronary arteries in X-ray angiograms is an important task in medical imaging, motivated by the desire to standardize the assessment of coronary artery disease and to facilitate the three-dimensional (3-D) reconstruction and visualization of the coronary vasculature, However, automatic labeling poses a number of significant challenges, including the presence of noise, artifacts, competing structures, misleading visual cues, and other difficulties associated with a dynamic and inherently complex structure.
We have developed a model-guided approach that addresses these challenges and automatically labels the vascular structure in coronary angiographic images. The approach consists of two models: 1) a symbolic model, represented through a directed acyclic graph, that captures vascular tree hierarchies and branch interrelationships and 2) a generalized 3-D model that captures spatial and geometric relationships. Importantly, the approach detects ambiguities (such as vessel overlaps) that may be found in a frame of a cine sequence, and resolves these ambiguities by considering the information derived from other (unambiguous) frames in the temporal sequence, employing dynamic programming methods to match the image features found in the different (ambiguous and unambiguous) frames. This paper presents this model-guided labeling algorithm and discusses the experimental results obtained from implementing and applying the resulting labeling system to a variety of clinical images. The results indicate the feasibility of achieving robust and consistently accurate image labeling through this model-guided, temporal disambiguation method.
|
Cited references:
| BAI ZD-1989-COMPUT-VISION-GRAPH-V47-P165
BALLARD D-1982-COMPUTER-VISION
BARBA J-1988-SPIE-V974-P389
CONNERS RW-1982-6-INT-C-PATT-REC-MUN
COPPINI G-1993-IEEE-T-PATTERN-ANAL-V15-P156
CORMEN TH-1990-ALGORITHMS
DODGE JT-1992-CIRCULATION-V86-P232
DUMAY ACM-1994-INT-J-CARDIAC-IMAG-V10-P205
DUMAY ACM-1992-P-11-IAPR-C-PATT-REC-V3-P439
ELION JL-1988-COMPUT-CARDIOL-P201
GARREAU M-1991-IEEE-T-MED-IMAGING-V10-P122
GEIGER D-1993-P-IEEE-C-COMP-VIS-PA-P602
HARALICK R-1992-COMPUTER-ROBOT-VISIO-V2
HARALICK R-1992-COMPUTER-ROBOT-VISIO-V1
HOFFMANN KR-1986-SPIE-MED-14-V626-P326
HYCHE M-1992-SPIE-V1808-P52
JAIN A-1989-FUNDAMENTALS-DIGITAL
KAYIKCIOGLU T-1993-SPIE-V1898-P62
KINDELAN M-DIGITAL-IMAGE-ANAL-P285
KITAMURA K-1988-IEEE-T-MED-IMAGING-V7-P173
LIU IC-1993-IEEE-T-MED-IMAGING-V12-P334
MARR D-1984-VISION
NEKOVEI R-1990-P-ANN-INT-C-IEEE-ENG-V12-P1459
NGUYEN TV-1994-IEEE-T-MED-IMAGING-V13-P61
OBRIEN J-1994-SPIE-VISUALIZATION-B-V2359
PEIFER JW-1990-IEEE-T-BIO-MED-ENG-V37-P744
PELLOT C-1994-IEEE-T-MED-IMAGING-V13-P48
PELLOT C-1992-MED-BIOL-ENG-COMPUT-V30-P576
RONG JH-1989-SPIE-V1137
SAITO T-1990-IEEE-T-BIO-MED-ENG-V37-P768
SMETS C-1990-INT-J-CARDIAC-IMAG-V5-P145
SONKA M-1993-IEEE-T-MED-IMAGING-V12-P588
SOUMEKH M-1988-IEEE-INT-C-AC-SPEECH-P1280
STANSFIELD SA-1986-IEEE-T-PATTERN-ANAL-V8-P188
SUN Y-1994-IEEE-T-PATTERN-ANAL-V16-P241
THACKRAY BD-1993-IEEE-T-MED-IMAGING-V12-P385
TOM BCS-1994-IEEE-T-MED-IMAGING-V13-P450
TRAN LV-1992-IEEE-T-MED-IMAGING-V11-P517
TSUJI S-1981-P-7-IJCAI-VANC-AUG-P710
VANDENELSEN PA-1993-IEEE-ENG-MED-BIO-MAR-P26
WINSTON PH-1984-ARTIFICIAL-INTELLIGE
YANAGIHARA Y-1994-INT-J-CARDIAC-IMAG-V10-P253
|
Times Cited:
| 2
|
Source item page count:
| 13
|
Publication Date:
| JUN
|
IDS No.:
| 115GZ
|
29-char source abbrev:
| IEEE TRANS MED IMAGING
|
Publisher address:
| 345 E 47TH ST, NEW YORK, NY 10017-2394 USA
|
Record 5 of 6
|
|
Author(s):
| Esthappan J; Harauchi H; Hoffmann KR
|
Title:
| Evaluation of imaging geometries calculated from biplane images
|
Source:
| MEDICAL PHYSICS 1998, Vol 25, Iss 6, pp 965-975
|
No. cited references:
| 32
|
ISSN/ISBN:
| 0094-2405
|
Publisher:
| AMER INST PHYSICS
|
Addresses:
| Esthappan J, Univ Chicago, Dept Radiol, Kurt Rossmann Labs Radiol Image Res, MC 2026,5841 S Maryland Ave, Chicago, IL 60637 USA Univ Chicago, Dept Radiol, Kurt Rossmann Labs Radiol Image Res, Chicago, IL 60637 USA Osaka Univ, Fac Med, Sch Allied Hlth Sci, Osaka 5650871, Japan
|
Author Keywords:
| 3D reconstruction; biplane; imaging geometry
|
KeywordsPlus:
| STEREOSCOPIC DSA SYSTEM; 3-DIMENSIONAL STRUCTURE; VIEWS; RECONSTRUCTION; ANGIOGRAPHY; MOTION
|
Abstract:
| A technique is developed that will calculate accurate and reliable imaging geometries and three-dimensional (3D) positions from biplane images of a calibration phantom. The calculated data provided by our technique will facilitate accurate 3D analysis in various clinical applications. Biplane images of a Lucite cube containing lead beads 1 mm in diameter were acquired. After identifying corresponding beads in both images and calculating their image positions, the 3D positions of the beads relative to each focal spot were determined. From these data, the transformation relating the 3D configurations were calculated to give the imaging geometry relating the biplane views. The 3D positions of objects were determined from the biplane images along with the corresponding imaging geometries. In addition, methods are developed to evaluate the quality of the calculated results on a case-by-case basis in the clinical setting. Methods are presented for evaluating the reproducibility of the calculated geometries and 3D positions, the accuracy of calculated object sizes, and the effects of errors due to time jitter, variation in user-indication, centering, and distortions on the calculated geometries and 3D reconstructions. The precision of the translation vectors and rotation matrices of the calculated geometries were within 1% and 1 degrees, respectively, in phantom studies, with estimated accuracies of approximately 0.5% and 0.4 degrees, respectively, in simulation studies. The precisions of the absolute 3D positions and orientations of the calculated 3D reconstructions were approximately 2 mm and 0.5 degrees, respectively, in phantom studies, with estimated accuracies of approximately 1.5 mm and 0.4 degrees, respectively, in simulation studies. This technique will provide accurate and precise imaging geometries as well as 3D positions from biplane images, thereby facilitating 3D analysis in various clinical applications. We believe that the study presented here is unique in that it represents the first steps toward understanding and evaluating the reliability of these 3D calculations in the clinical situation. (C) 1998 American Association of Physicists in Medicine. [S0094-2405(98)01606-X].
|
Cited references:
| CHEN SJ-1996-IEEE-COMPUTERS-CARDI-P117
CHEN SYJ-1997-MED-PHYS-V24-P633
CHEN SYJ-1997-P-SOC-PHOTO-OPT-1&2-V3034-P358
CHEN SYJ-1996-P-SOC-PHOTO-OPT-INS-V2710-P103
CHERIET F-1996-IEEE-COMPUTERS-CARDI-P409
DUMAY ACM-1994-IEEE-T-MED-IMAGING-V13-P13
FENCIL LE-1988-INVEST-RADIOL-V23-P33
FENCIL LE-1990-MED-PHYS-V17-P951
FENCIL LE-1989-PHYS-MED-BIOL-V34-P659
HARRIGAN T-1996-J-AM-COLL-CARDIOL-V27-PA345
HOFFMANN KR-1996-IEEE-COMPUTERS-CARDI-P113
HOFFMANN KR-1997-MED-PHYS-V24-P555
HOFFMANN KR-1997-MED-PHYS-V24-P1854
HOFFMANN KR-1995-MED-PHYS-V22-P1219
HOFFMANN KR-1996-P-SOC-PHOTO-OPT-INS-V2710-P462
HOFFMANN KR-1996-P-SOC-PHOTO-OPT-INS-V2708-P371
KASSAEE A-1994-MED-PHYS-V21-P643
KELLER PJ-1989-RADIOLOGY-V173-P527
LI S-1996-MED-PHYS-V23-P921
MACKAY SA-1982-COMPUT-BIOMED-RES-V15-P455
METZ CE-1989-MED-PHYS-V16-P45
MOL CR-1984-124-IBM-UKSC
NAPEL S-1992-RADIOLOGY-V185-P607
NGUYEN TV-1994-IEEE-T-MED-IMAGING-V13-P61
ROUGEE A-1993-P-SPIE-MED-IMAGING-V1897-P161
SCHONEMANN P-1966-PSYCHOMETRIKA-V31-P1
SCHONEMANN PH-1970-PSYCHOMETRIKA-V35-P245
SCHREINER S-1997-P-SOC-PHOTO-OPT-INS-V3031-P160
TOWLE VL-1995-ELECTROEN-CLIN-NEURO-V94-P221
WAHLE A-18-ANN-INT-C-IEEE-EN
WAHLE A-1995-COMPUTER-ASSISTED-RA-P208
WAHLE A-1995-IEEE-T-MED-IMAGING-V14-P230
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Times Cited:
| 1
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Source item page count:
| 11
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Publication Date:
| JUN
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IDS No.:
| ZV222
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29-char source abbrev:
| MED PHYS
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Publisher address:
| CIRCULATION FULFILLMENT DIV, 500 SUNNYSIDE BLVD, WOODBURY, NY 11797-2999 USA
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Record 6 of 6
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Author(s):
| Hall P; Ngan M; Andreae P
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Title:
| Reconstruction of vascular networks using three-dimensional models
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Source:
| IEEE TRANSACTIONS ON MEDICAL IMAGING 1997, Vol 16, Iss 6, pp 919-929
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No. cited references:
| 30
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ISSN/ISBN:
| 0278-0062
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Publisher:
| IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
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Addresses:
| Hall P, Univ Wales, Dept Comp Sci, POB 916, Cardiff CF2 3XF, S Glam, Wales Univ Wales, Dept Comp Sci, Cardiff CF2 3XF, S Glam, Wales Victoria Univ Wellington, Dept Comp Sci, Wellington, New Zealand
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Author Keywords:
| cerebral vasculature; DSA; reconstruction; representation
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KeywordsPlus:
| 3-D RECONSTRUCTION; ANGIOGRAMS; KNOWLEDGE; SYSTEM; TREES
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Abstract:
| Reconstructing vasculature in three dimensions is a challenging problem, Early approaches concentrated on coronary vasculature in X-ray images, recent work uses magnetic resonance imagery of cerebral vasculature. In both cases a priori information has been used, and often the way this is represented has proven limiting to the scope of applications supported, For example, a particular representation may be useful only for X-ray images, This paper addresses two issues: 1) representing a collection of vasculature and 2) the reconstruction of individual vasculature from images, Our representation learns the variations in branching structures and vessel shapes that occur between individuals, It supports a vascular catalogue containing three-dimensional (3-D) anatomical models. The representation is task independent; here we use it to reconstruct vasculature from images, Our algorithm has four features to which we draw attention: 1) it is not premised wholly upon X-ray images (though that is our focus here); 2) it produces several feasible solutions rather than one; 3) it can generalize from the catalogue to reconstruct instances not yet learned; 4) it exhibits polynomial time complexity, reasonable memory consumption, and is reliable, Both our representation and reconstruction algorithm are new and useful approaches, In support of these claims,,ve present results gathered from X-rays of both simulated and real vasculature.
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Cited references:
| AMIT Y-1997-IEEE-T-MED-IMAGING-V16-P28
BARILLOT C-1985-IEEE-COMPUT-GRAPH-V22-P13
COPPINI G-1993-IEEE-T-PATTERN-ANAL-V15-P156
DELAERE D-1990-P-N-SEA-C-BIOM-ENG-N-PNS27
FESSLER JA-1991-IEEE-T-MED-IMAGING-V10-P25
GARREAU M-1991-IEEE-T-MED-IMAGING-V10-P122
HALL PM-1996-EUR-C-COMP-VIS-P293
HALL PM-1996-MATH-MODELING-SCI-CO-V6
HALL PM-1995-NZ-J-COMPUT-V6-P253
HALL PM-1997-P-BRIT-MACH-VIS-C-SE-P300
HALL PM-1993-P-DICTA-93-P414
HIGGINS WE-1996-IEEE-T-MED-IMAGING-V15-P377
HOFFMANN KR-SPIE-V767-P449
HOFFMANN KR-1986-SPIE-MED-14-V626-P326
KOLLER T-1995-MODELING-INTERACTION
KUTA R-1996-IEEE-T-MED-IMAGING-V15-P51
LAVAYSSIERE B-1987-P-COMPUTER-ASSISTED-P225
MCKEVITT P-1996-ARTIF-INTELL-REV-V10-P235
METZ CE-1989-MED-PHYS-V16-P45
NGAN M-1996-THESIS-U-WELLINGTON
NGUYEN TV-1994-IEEE-T-MED-IMAGING-V13-P61
PELLOT C-1994-IEEE-T-MED-IMAGING-V13-P48
RAKE ST-1987-P-INT-S-CAR-P681
SALAMON G-1976-RADIOLOGICAL-ANATOMY
SONKA M-1997-IEEE-T-MED-IMAGING-V16-P87
SUETENS P-SPIE-V767-P454
SUN Y-1988-INT-C-AC-SPEECH-SIGN-V2-P1296
SZEKELY G-1995-ST-HEAL-T-V19-P183
WAHLE A-1995-IEEE-T-MED-IMAGING-V14-P230
WINSTON PH-1984-ARTIFICIAL-INTELLIGE
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Times Cited:
| 6
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Source item page count:
| 11
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Publication Date:
| DEC
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IDS No.:
| ZB313
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29-char source abbrev:
| IEEE TRANS MED IMAGING
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Publisher address:
| 345 E 47TH ST, NEW YORK, NY 10017-2394 USA
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